Zhengyang WAN

Ph.D. student at The University of Wisconsin-Madison CEE
Zhengyang Wan in Chicago

Chicago, US, 2025

Hi! I am a Ph.D. student in the Department of Civil and Environmental Engineering at the University of Wisconsin–Madison. I joined Sky-Lab in Fall 2025 under the guidance of Dr. Sikai (Sky) Chen. Before that, I obtained my B.S. and M.S. from Tongji University.

I work on autonomous driving and human-centered intelligent transportation through simulation, real-vehicle testing, and human-AI collaboration. I contribute to SkyDrive, Sky-Lab’s open-source multi-agent platform for future transportation systems and autonomous driving.

My research focuses on:

Autonomous driving

  • Closed-loop simulation and sim-to-real transfer
  • Vision-Language-Action (VLA) models for driving

Human-centric intelligent transportation

  • Human-AI collaborative driving and interaction
  • Socially-aware multi-agent systems
Placeholder — closed-loop simulation and sim-to-real transfer
Closed-loop simulation & sim-to-real
Placeholder — Vision-Language-Action models for driving
Vision-Language-Action models
Placeholder — human-AI collaborative driving
Human-AI collaborative driving
Placeholder — socially-aware multi-agent systems
Socially-aware multi-agent systems

Selected Publications

  1. arXiv
    Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving
    Featured
    Sim2Real-AD: A Modular Sim-to-Real Framework for Deploying VLM-Guided Reinforcement Learning in Real-World Autonomous Driving
    Zilin Huang, Zhengyang Wan, Zihao Sheng, and

    Sim2Real-AD is a modular framework for transferring CARLA-trained, VLM-guided reinforcement-learning policies to full-scale vehicles without real-world RL training data. It supports zero-shot real-world deployment despite simulator-specific observation and action-semantics mismatches.

    arXiv preprint arXiv:2604.03497 · 2026
    Paper
    Scholar
  2. arXiv
    DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
    Featured
    DriveVLM-RL: Neuroscience-Inspired Reinforcement Learning with Vision-Language Models for Safe and Deployable Autonomous Driving
    Zilin Huang, Zihao Sheng, Zhengyang Wan, and

    DriveVLM-RL combines a CLIP-based static safety pathway with attention-gated multi-frame VLM reasoning to learn semantic rewards for autonomous driving. Its asynchronous training pipeline removes all VLM components at deployment while improving collision avoidance, task success, and generalization in CARLA.

    arXiv preprint arXiv:2603.18315 · 2026
    Paper
    Scholar 4
  3. JICV
    Sky-Drive: A Distributed Multi-Agent Simulation Platform for Socially-Aware and Human-AI Collaborative Future Transportation
    Featured
    Sky-Drive: A Distributed Multi-Agent Simulation Platform for Socially-Aware and Human-AI Collaborative Future Transportation
    Zilin Huang*, Zihao Sheng*, Zhengyang Wan*, and

    Sky-Drive is a distributed multi-agent simulation platform for socially aware driving and human-AI collaboration. It combines synchronized multi-terminal simulation, multimodal human-in-the-loop data collection, adaptive human-AI knowledge exchange, and digital twins of real transportation environments.

    Journal of Intelligent and Connected Vehicles · 2025
    Paper
    Scholar 22 GitHub 9 ★
  4. J. Automob. Eng.
    Study of the Tire Wear of Virtual Track Train
    Featured
    Study of the Tire Wear of Virtual Track Train
    Hechao Zhou, Zhengyang Wan, Mingsu Mei, and

    This study develops and validates a multibody dynamics model of a Virtual Track Train together with a finite-element tire model to calculate tire wear. The results identify tire pressure, vehicle load, pavement conditions, inclination angle, and tire slippage as critical influences on wear.

    Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering · 2024
    Paper
    Scholar

All publications

Recent News

  1. I passed my Ph.D. qualifying exam — a big milestone on my doctoral journey! 🎓
  2. My master’s thesis “Study of the curve passing performance of the Virtual Track Train based on driving simulation platform” is selected as the outstanding master’s thesis of Tongji University (Top 5%). Cheers! 🥳
  3. I joined UW-Madison as a Ph.D. student at Sky-Lab.🏎️💨
  4. Happy Graduation!!🧑‍🎓 What a journey it is! I will miss my time at Tongji.🥹🥹